Block Model - Newman's Clustering Algorithm
Network Level Measures
Measure Value Row count 812.000 Column count 812.000 Link count 1719.000 Density 0.003 Components of 1 node (isolates) 231 Components of 2 nodes (dyadic isolates) 2 Components of 3 or more nodes 1 Reciprocity 0.034 Characteristic path length 3.685 Clustering coefficient 0.200 Network levels (diameter) 10.000 Network fragmentation 0.495 Krackhardt connectedness 0.505 Krackhardt efficiency 0.993 Krackhardt hierarchy 0.783 Krackhardt upperboundedness 0.743 Degree centralization 0.047 Betweenness centralization 0.031 Closeness centralization 0.001 Eigenvector centralization 0.327 Reciprocal (symmetric)? No (3% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.049 0.003 0.006 Total degree centrality [Unscaled] 0.000 160.000 8.384 18.533 In-degree centrality 0.000 0.067 0.003 0.007 In-degree centrality [Unscaled] 0.000 108.000 4.234 10.875 Out-degree centrality 0.000 0.052 0.003 0.006 Out-degree centrality [Unscaled] 0.000 84.000 4.234 9.076 Eigenvector centrality 0.000 0.348 0.022 0.045 Eigenvector centrality [Unscaled] 0.000 0.246 0.016 0.031 Eigenvector centrality per component 0.000 0.175 0.011 0.022 Closeness centrality 0.001 0.002 0.002 0.000 Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 In-Closeness centrality 0.001 0.003 0.002 0.001 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.032 0.001 0.003 Betweenness centrality [Unscaled] 0.000 20701.434 480.592 1908.707 Hub centrality 0.000 0.351 0.021 0.045 Authority centrality 0.000 0.464 0.018 0.046 Information centrality 0.000 0.004 0.001 0.001 Information centrality [Unscaled] 0.000 4.230 1.273 1.175 Clique membership count 0.000 138.000 2.794 11.728 Simmelian ties 0.000 0.002 0.000 0.000 Simmelian ties [Unscaled] 0.000 2.000 0.007 0.121 Clustering coefficient 0.000 1.000 0.200 0.338
Key Nodes
This chart shows the Agent that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Agent was ranked in the top three.
Total degree centrality
The Total Degree Centrality of a node is the normalized sum of its row and column degrees. Individuals or organizations who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are "in the know" are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.
Input network: Daughter (size: 812, density: 0.00260714)
Rank Agent Value Unscaled Context* 1 & 0.049 160.000 26.088 2 Poca 0.047 152.000 24.710 3 Mence 0.044 142.000 22.989 4 Marinus 0.041 132.000 21.267 5 Resti 0.038 124.000 19.890 6 Caboga 0.038 124.000 19.890 7 Bincola 0.038 122.000 19.546 8 Basilio 0.036 118.000 18.857 9 Getaldi 0.030 96.000 15.070 10 Petrus 0.028 92.000 14.381 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.003 Mean in random network: 0.003 Std.dev: 0.006 Std.dev in random network: 0.002 In-degree centrality
The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual or a resource, the in-links are the connections that the node of interest receives from other nodes. For example, imagine an agent by knowledge matrix then the number of in-links a piece of knowledge has is the number of agents that are connected to. The scientific name of this measure is in-degree and it is calculated on the agent by agent matrices.
Input network(s): Daughter
Rank Agent Value Unscaled 1 & 0.067 108.000 2 Poca 0.064 104.000 3 Mence 0.050 82.000 4 Bincola 0.049 80.000 5 Basilio 0.047 76.000 6 Caboga 0.046 74.000 7 Marinus 0.041 66.000 8 Resti 0.039 64.000 9 Saraca 0.032 52.000 10 Grede 0.032 52.000 Out-degree centrality
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest sends to other nodes. For example, imagine an agent by knowledge matrix then the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it is calculated on the agent by agent matrices. Individuals or organizations who are high in most knowledge have more expertise or are associated with more types of knowledge than are others. If no sub-network connecting agents to knowledge exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals or organizations who are high in "most resources" have more resources or are associated with more types of resources than are others. If no sub-network connecting agents to resources exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by resource matrices.
Input network(s): Daughter
Rank Agent Value Unscaled 1 Goce 0.052 84.000 2 Marinus 0.041 66.000 3 Mence 0.038 62.000 4 Resti 0.038 62.000 5 Bona 0.034 56.000 6 Georgio 0.034 56.000 7 Getaldi 0.033 54.000 8 & 0.033 54.000 9 Caboga 0.032 52.000 10 Poca 0.031 50.000 Eigenvector centrality
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Leaders of strong cliques are individuals who or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent matrices.
Input network: Daughter (size: 812, density: 0.00260714)
Rank Agent Value Unscaled Context* 1 Mence 0.348 0.246 -0.295 2 Poca 0.333 0.235 -0.394 3 & 0.325 0.230 -0.443 4 Basilio 0.309 0.218 -0.551 5 Marinus 0.307 0.217 -0.560 6 Bincola 0.287 0.203 -0.695 7 Caboga 0.272 0.192 -0.792 8 Resti 0.249 0.176 -0.941 9 Getaldi 0.243 0.172 -0.977 10 Petrus 0.234 0.166 -1.036 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.022 Mean in random network: 0.393 Std.dev: 0.045 Std.dev in random network: 0.154 Eigenvector centrality per component
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Each component is extracted as a separate network, Eigenvector Centrality is computed on it and scaled according to the component size. The scores are then combined into a single result vector.
Input network(s): Daughter
Rank Agent Value 1 Mence 0.175 2 Poca 0.167 3 & 0.163 4 Basilio 0.155 5 Marinus 0.154 6 Bincola 0.144 7 Caboga 0.137 8 Resti 0.125 9 Getaldi 0.122 10 Petrus 0.118 Closeness centrality
The average closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the average distance in the network from the node to all other nodes.
Input network: Daughter (size: 812, density: 0.00260714)
Rank Agent Value Unscaled Context* 1 Bona 0.002 0.000 -5.354 2 Sorgo 0.002 0.000 -5.355 3 Gondola 0.002 0.000 -5.355 4 Georgio 0.002 0.000 -5.356 5 Zamagna 0.002 0.000 -5.356 6 Raphael 0.002 0.000 -5.356 7 Antonio 0.002 0.000 -5.356 8 Franchus 0.002 0.000 -5.356 9 Federicus 0.002 0.000 -5.356 10 Mattheus 0.002 0.000 -5.356 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.002 Mean in random network: 0.100 Std.dev: 0.000 Std.dev in random network: 0.018 In-Closeness centrality
The average closeness of a node from the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network to the node and from all other nodes.
Input network(s): Daughter
Rank Agent Value Unscaled 1 Matteo 0.003 0.000 2 Jachussa 0.003 0.000 3 Scocilcha 0.003 0.000 4 Lisa 0.003 0.000 5 Nicxa 0.003 0.000 6 Vale 0.003 0.000 7 Paocho 0.003 0.000 8 Prodani 0.003 0.000 9 Slavussa 0.003 0.000 10 Catena 0.003 0.000 Betweenness centrality
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
Input network: Daughter (size: 812, density: 0.00260714)
Rank Agent Value Unscaled Context* 1 Poca 0.032 20701.434 0.083 2 & 0.028 18202.230 0.071 3 Marinus 0.027 17606.590 0.068 4 Resti 0.024 15841.832 0.060 5 Mence 0.024 15784.303 0.060 6 Basilio 0.022 14568.860 0.054 7 Bincola 0.022 14177.950 0.052 8 Caboga 0.016 10718.490 0.036 9 Petrus 0.016 10471.125 0.035 10 Getaldi 0.014 9111.461 0.029 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.001 Mean in random network: 0.004 Std.dev: 0.003 Std.dev in random network: 0.329 Hub centrality
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
Input network(s): Daughter
Rank Agent Value 1 Goce 0.351 2 Marinus 0.341 3 Mence 0.314 4 Bincola 0.296 5 Resti 0.291 6 Bona 0.271 7 Caboga 0.270 8 & 0.266 9 Getaldi 0.260 10 Petrus 0.246 Authority centrality
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others. Technically, an agent is authority-central if its in-links are from agents that have are sending links to many others. The scientific name of this measure is authority centrality and it is calculated on agent by agent matrices.
Input network(s): Daughter
Rank Agent Value 1 & 0.464 2 Poca 0.423 3 Mence 0.365 4 Basilio 0.352 5 Bincola 0.319 6 Caboga 0.317 7 Marinus 0.280 8 Resti 0.231 9 Dersa 0.221 10 Grede 0.216 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Daughter
Rank Agent Value Unscaled 1 Goce 0.004 4.230 2 Marinus 0.004 4.168 3 Resti 0.004 4.156 4 Mence 0.004 4.150 5 Bona 0.004 4.126 6 Georgio 0.004 4.126 7 Getaldi 0.004 4.115 8 & 0.004 4.115 9 Caboga 0.004 4.099 10 Poca 0.004 4.067 Clique membership count
The number of distinct cliques to which each node belongs. Individuals or organizations who are high in number of cliques are those that belong to a large number of distinct cliques. A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. The scientific name of this measure is clique count and it is calculated on the agent by agent matrices.
Input network(s): Daughter
Rank Agent Value 1 Mence 138.000 2 Marinus 120.000 3 & 111.000 4 Poca 106.000 5 Basilio 105.000 6 Caboga 87.000 7 Bincola 75.000 8 Resti 58.000 9 Petrus 56.000 10 Goce 53.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Daughter
Rank Agent Value Unscaled 1 Maria 0.002 2.000 2 Petrana 0.002 2.000 3 & 0.002 2.000 Clustering coefficient
Measures the degree of clustering in a network by averaging the clustering coefficient of each node, which is defined as the density of the node's ego network.
Input network(s): Daughter
Rank Agent Value 1 Gaislava 1.000 2 Johannis 1.000 3 Dergmiri 1.000 4 Valius 1.000 5 Jure 1.000 6 Bogde 1.000 7 Biaxio 1.000 8 Dime 1.000 9 Givchus 1.000 10 Matcho 1.000
Key Nodes Table
This shows the top scoring nodes side-by-side for selected measures.
Rank Betweenness centrality Closeness centrality Eigenvector centrality Eigenvector centrality per component In-degree centrality In-Closeness centrality Out-degree centrality Total degree centrality 1 Poca Bona Mence Mence & Matteo Goce & 2 & Sorgo Poca Poca Poca Jachussa Marinus Poca 3 Marinus Gondola & & Mence Scocilcha Mence Mence 4 Resti Georgio Basilio Basilio Bincola Lisa Resti Marinus 5 Mence Zamagna Marinus Marinus Basilio Nicxa Bona Resti 6 Basilio Raphael Bincola Bincola Caboga Vale Georgio Caboga 7 Bincola Antonio Caboga Caboga Marinus Paocho Getaldi Bincola 8 Caboga Franchus Resti Resti Resti Prodani & Basilio 9 Petrus Federicus Getaldi Getaldi Saraca Slavussa Caboga Getaldi 10 Getaldi Mattheus Petrus Petrus Grede Catena Poca Petrus